Ai/ml Language Model Dev
3 days ago
**AI/ML Engineer - Language Model Developer**
This role is for an experienced Artificial Intelligence Engineer on the Marketing Data Science team at HP. This individual will be responsible for helping the team to shape its AI strategy, specifically through the creation of AI models, such as Large Language models (LLMs) and Natural Language Processing (NLP), for Marketing. Marketers are tasked with combing through tons of research and data to find appropriate insights; there is oftentimes too much information and too little time. This individual will provide Marketers with curated AI tools that help them to have the right information at the right time to make the best decisions. This is a fantastic opportunity to be part of cutting-edge AI work that enables Marketing to drive innovation and business growth.
**Locations**:Bucharest, Romania, On-site or Hybrid
**Education and Experience**:
- MS in Computer Science with an AI emphasis, or related field
- Experience with cloud environments (Amazon Web Services, Google Cloud, Azure, Databricks)
**Responsibilities**:
- Design the architecture and environment to enable AI models to run
- Assess which environments are most appropriate, whether current compute is sufficient, whether we should choose an existing model or pretrain our own
- Lead AI model construction to both evolve existing capabilities and create new ones
- Teach Data Scientists how to use the created LLMs to analyze structured and unstructured data
- Advise the team on best practices for AI models (i.e. parameters, data structure, etc.)
- Debug and identify causality for issues in the AI results
- Research new AI models and trends to understand how we can maximize the usage of our existing models and/or whether we should adopt new models
**Knowledge and Skills**:
- Strong knowledge of ML and Deep Learning in the context of LLMs, NLPs and chatbots
- Experience working with AI models using real-life (“industry”) data
- Expert Python skills (PyTorch/TensorFlow, Pandas, NumPy, Matplotlib, creating functions)
- Mastery in tool usage (AWS, Jupyter notebook, Streamlit or similar UIs)
- Expertise in building and fine-tuning LLMs; experience with PEFT methods and alignment with human feedback (RLHF)
- Expertise in Prompt Engineering using various AI models
- Understanding of the differences and advantages of Llama, Falcon, and other open source LLMs
- Experience utilizing structured and unstructured data in LLMs (CSV, Json, TXT, PDF)
- Mastery in database engineering (Relational and Vector databases/Embeddings)
- Proficiency in AI frameworks (Langchain, Agents, Transformer Architecture etc.)
- Experience utilizing both Generative and Discriminative AI models for data querying and representation
- In-depth knowledge of big data platforms, statistical software and machine learning algorithms packages
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